The present invention relates generally to wayside equipment of railroad tracks and more particularly to the inspection of signaling equipment installed close to or on a railroad track, such as grade crossing signaling equipment or point machines.
US federal regulations require periodic inspection of grade crossings and their equipment. The inspections typically involve trained personnel, which has to go to the grade crossing and visually inspect the crossing and the equipment. The personnel must check whether signaling lamps are properly aimed and crossing gates are working properly. This is time consuming and expensive.
Crossing gates must be precisely positioned and signaling lamps must be precisely aimed so that the maximum light intensity is within the field of vision of a motor vehicle operator in the road. On train approach the lamps flash and the gates are lowered within a defined amount of time. Currently, imprecise aiming of lights or changes to operating positions of the gates are undetectable except through human inspection.
In an attempt to reduce human intervention and inspection costs, the Office of Research, Development and Technology (ORDT) of the US Federal Railroad Administration (FRA) has developed an automated grade crossing survey system in cooperation with the University of Michigan and the company ENSCO, Inc. This system is detailed in the article “The Federal Railroad Administration's Automated Grade Crossing Survey System” by Soheil Saadat et al published in the Proceedings of the American Railway Engineering and Maintenance-of-Way Association (AREMA), 2015, which can be downloaded at https://april.eecs.umich.edu/papers/details.php?name=saadat2015.
This known system consists of a set of LiDAR sensors installed on a gage restraint measurement rail car. When the rail car travels through a grade crossing, the LiDAR sensors scan the grade crossing and its surroundings. The scan creates a point cloud representation of the grade crossing. The point cloud is used to assess the surface profile of the grade crossing in order to detect humped grade crossings that might be prone to hang-up incidents with heavy motor vehicles.
A similar system using a modified surveying truck is described in the article “Automated Safety Inspection of Grade Crossings” by Pradeep Ranganathan et al., presented at the 2010 International Conference on Intelligent Robots and Systems (IROS), which can be downloaded at https://www.semanticscholar.org/paper/Automated-safety-inspection-of-grade-crossings-Ranganathan-Oison/0a0d4055a20170c94972229ee9e76af4b539ff22.
These known moving systems with travel-by inspection are neither designed nor adapted to check the exact alignment and proper operation of grade crossing equipment such as signaling lamps and crossing barriers. Indeed, since the LiDAR sensors are installed on a moving vehicle, the point clouds obtained from a grade crossing will be subject to noise generated by the movement of the vehicle. Such noise prevents the detection of small misalignments in the grade crossing equipment. Also, the LiDAR system can only make a short snapshot of the grade crossing while traveling by. Hence, a prolonged movement of e.g. a crossing gate when it moves from its raised position to its lowered position cannot be captured and analyzed.
Grade crossing inspection might also be performed with video imaging and analysis. However, video analytics do not provide distance and are susceptible to weather and lighting conditions.